The negative consequences of poor data quality are very important for organizations, but fortunately they are also easy to combat. While data quality is one of the issues discussed for a long time, the production factor, i.e. keeping data in shape, can sometimes be challenging. Disruptions in production processes, unreliable information on overviews, dissatisfied customers and feedback are recognizable problems that increase the cost for entrepreneurs. This is usually related to the use of incorrect data. By making an organization data-driven and paying attention to data quality policies, you can avoid these problems, save costs, and explore new opportunities. The quality of the data is also assessed by the purpose of use. Because it is true if a data allows you to achieve the result you want to achieve. Therefore, the accuracy of the data actually means the same as the quality of the data. So what is data quality really, what is it for, and why is it important? Also, what are the steps that need to be followed to ensure data quality?

What is Data Quality?

It is difficult to make a clear definition of data quality. The truth is that your data quality is good if the data achieves its purpose of using it. For example, showing the right values on a management board to guide the organization ensures that management is also consistent and the process is managed correctly. The quality of the data can be measured under some rules. There are eight aspects identified by the Data Management Community to measure data quality. Here are the guidelines for measuring data quality:

· Being complete: Has all the data been entered, is there any missing information?

· Uniqueness: Are there duplicate values in the data?

· Time consistency: Is accurate data requested for a specific point in time?

· Validity: Was the data entered according to the established rules?

· Accuracy: Is the data entered accurate? Does the data accurately reflect the truth?

· Consistency: Is the data the same across different storage locations?

· Clarity: Is the data open to interpretation, or does it just mean something?

· Relevance: Is the data exactly related to the user and the purpose of use?

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Why is Data Quality Important?

If the data quality is poor, obtaining the necessary information can be very costly. In addition, inaccurate data can lead to cost loss in many matters, especially accounting. For example, incorrect entry of sales figures, late writing of repair dates of devices requiring repair and maintenance. There are many points to consider in this regard. From the use of decimal numbers to the use of commas or dots, to the insertion of symbols such as € or $ in the input fields, all the details need to be taken into account. It can be quite difficult to correct sales figures accurately, especially if there are no standards or retroactive conversions. Finding out what needs to be filled and repairing it takes a lot of time and money.

Having your data quality in order gives you confidence, so you can be sure that what you see is real and everyone can trust the data. If employees can not trust the data, you notice that they start tracking everything in individual Excel sheets, which means that the overview is lost and creates a waste of time. For example, a marketing agency needs address information for a marketing campaign, so it is especially important that this address information is current, complete and accurate. In the event that an organization reports its annual financial figures, in particular, its financial data must be accurate and complete.

What Are the Benefits of Data Quality?

Data quality is the extent to which it is suitable for the purpose for which you want to use it. But if a data meets eight aspects such as accuracy, time, consistency, etc., it means quality. It gives you many benefits if you are sure that your data is of good quality. The main benefits of data quality can be listed as follows:

· Decision making: The better the quality of data, the more confident employees are in the results they produce, reducing risk in results and increasing efficiency. When the results are reliable, the risk in guessing and decision-making can be reduced.

· Productivity: Quality data makes employees more productive. Instead of spending time verifying and repairing data errors, they can focus on their core tasks.

· Compliance: In sectors where legislatures determine how relationships with customers or business activities will take place, good data quality can significantly prevent financial losses. Because harmony between these data is very important.

· Marketing: Better data enables accurate targeting and effective customer communication, especially in multi-channel environments where many organizations operate or want to work.

· Competitive Advantage: Good data quality can provide competitive advantage because an organization gains better insight about customers, products, and processes and can identify market opportunities faster.

· Financial gain: If an organization takes data quality seriously, returns also improve noticeably, resulting in the institution becoming more valuable. Of course, he gets more profit.

Steps to Follow to Improve Data Quality

In order for any data to be stored in a quality way, it is important to first determine what the purpose of the initiative is and for which data you started the initiative. You can focus on it specifically by identifying early on which data you find important. You can then take advantage of these tips to see where you can improve data quality:

· Pre-determining the format of certain fields, such as street, number, and apartment, in address entries, or specifying the sales profit as a percentage, can ensure that people fill in the data accurately and completely. Prioritizing accurate and important areas improves data quality.

· Internal control of recently entered data can help ensure that data is entered into the system correctly. A plus of this is that it is possible to immediately check whether this data was previously in the system. This way you will also avoid duplicate data.

· In addition to checking the most recently entered data, it is also necessary to ensure continuous improvement of data quality. For this, certain responsibilities can be assigned to various people within the organization. Having one or more people responsible for data in the organization reduces the risk of errors.

· It can also help establish connections between systems so that data is consistently found in both systems and is immediately associated with the correct data at the same time. But at this stage it is important that you decide which system is the leading source for certain data.

· Determine the right improvement method for the organisation. Based on this step, various options for improving data quality can be explored, including data cleansing, data mining, and data governance. Often data quality issues are the reason BI initiatives fail.il.

Data Quality Use Cases

The necessity of data quality and the benefits that can be obtained are usually obvious, since people use data to manage business processes, provide information to auditors, or measure progress in company objectives. People may also want to be able to make predictions with data for purposes such as knowing whether a product/service will yield as much return two years from now as it does today. Because this data is very important for an organization to set its business goals in the long term and be able to make the right decisions.

For example, there is a noticeable user difference between a mobile phone and a landline phone. The fact that a telecommunications provider knows who can offer a landline phone as a service/product to is a big plus in terms of business deals. Due to the increasing number of mobile phone subscriptions, customers' need for landline phones can also be expected to decrease/increase. However, this is an assumption and can cause losses as well as gains. When the data is verified, there is no doubt and you can take the right steps. Since it is supported by data, you can adjust your product strategy to the demands. If you want to be sure of the accuracy and security of the data, you can use Komtaş services to improve the data and make the right decisions for the future of your company.

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